Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology

Cell simulation as cell segmentation

Single-cell spatial transcriptomics promises a highly detailed view of a cell's transcriptional state and microenvironment, yet inaccurate cell segmentation can render these data murky by misattributing large numbers of transcripts to nearby cells or conjuring nonexistent cells. We adopt methods from ab initio cell simulation, in a method called Proseg (probabilistic segmentation), to rapidly infer morphologically plausible cell boundaries. Benchmarking applied to datasets generated by three commercial platforms shows superior performance and computational efficiency of Proseg when compared to existing methods. We show that improved accuracy in cell segmentation aids greatly in detection of difficult-to-segment tumor-infiltrating immune cells such as neutrophils and T cells. Last, through improvements in our ability to delineate subsets of tumor-infiltrating T cells, we show that CXCL13-expressing CD8+ T cells tend to be more closely associated with tumor cells than their CXCL13-negative counterparts in data generated from samples from patients with renal cell carcinoma.

Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 590 times compared to CPM code execution. Over multiple recursive evaluations, our model effectively captures the emergent behaviors demonstrated by the original Cellular-Potts model of such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM of biological processes at greater spatial and temporal scales.

Towards an integrated view and understanding of embryonic signalling during murine gastrulation

At the onset of mammalian gastrulation, secreted signalling molecules belonging to the Bmp, Wnt, Nodal and Fgf signalling pathways induce and pattern the primitive streak, marking the start for the cellular rearrangements that generate the body plan. Our current understanding of how signalling specifies and organises the germ layers in three dimensions, was mainly derived from genetic experimentation using mouse embryos performed over many decades. However, the exact spatiotemporal sequence of events is still poorly understood, both because of a lack of tractable models that allow for real time visualisation of signalling and differentiation and because of the molecular and cellular complexity of these early developmental events. In recent years, a new wave of in vitro embryo models has begun to shed light on the dynamics of signalling during primitive streak formation. Here we discuss the similarities and differences between a widely adopted mouse embryo model, termed gastruloids, and real embryos from a signalling perspective. We focus on the gene regulatory networks that underlie signalling pathway interactions and outline some of the challenges ahead. Finally, we provide a perspective on how embryo models may be used to advance our understanding of signalling dynamics through computational modelling.

Proteome of amino acids or IGF1-stimulated pacu muscle cells offers molecular insights and suggests FN1B and EIF3C as candidate markers of fish muscle growth

Study of fish skeletal muscle is essential to understand physiological or metabolic processes, and to develop programs searching for increased muscle mass and meat production. Amino acids (AA) and IGF1 stimulate processes that lead to muscle growth, but their signaling pathways and molecular regulation need further clarification in fish. We obtained the proteome of pacu (Piaractus mesopotamicus) cultured muscle cells treated with AA or IGF1, which induced the differential abundance of 67 and 53 proteins, respectively. Enrichment analyses showed that AA modulated histone methylation, cell differentiation, and metabolism, while IGF1 modulated ATP production and protein synthesis. In addition, we identified molecular networks with candidate markers that commonly regulate fish muscle cells: FN1B and EIF3C, respectively up- and down-regulated by both treatments. FN1B was related to cell proliferation, protein synthesis, and muscle repair, while EIF3C connected with negative regulators of muscle growth. Their gene expression was evaluated in pacu and Nile tilapia (Oreochromis niloticus) after nutrient manipulation, with fn1b increased during refeeding and eif3c increased during fasting in both species. Our work helps clarify the molecular regulation by AA or IGF1 and suggests that FN1B and EIF3C could be potential stimulatory and inhibitory biomarkers of fish muscle growth.

CAG Repeat Instability and Region-Specific Gene Expression Changes in the SCA12 Brain

Spinocerebellar ataxia type 12 (SCA12), an autosomal dominant cerebellar ataxia, caused by an expansion of (CAG)n in the 5' of the PPP2R2B gene on chr5q32, is common in India. The illness often manifests late in life, with diverse neurological and psychiatric symptoms, suggesting involvement of different brain regions. Prominent neuronal loss and atrophy of the cerebellum have been noted earlier. In Huntington's disease (HD), somatic instability associated with the size of the expanded CAG allele in HTT varies across regions of the brain, and influences the nature and severity of symptoms. We estimated CAG repeat size, methylation and gene expression in the PPP2R2B gene across regions in brain tissue from a person with SCA12. We also studied the regional expression of DNA repair pathway and cell cycle genes. Somatic mosaicism, manifested as CAG repeat instability, is detected across brain regions. The cerebellum showed the least somatic instability, and this was coupled with increased methylation, and lower expression, of the PPP2R2B gene. Interestingly, increased expression of DNA maintenance pathway related genes, which might partly explain the lowered DNA instability, was also observed. There was also decreased expression of cell cycle modulators, which could initiate apoptosis, and thus account for neuronal cell death seen in the brain sections. We suggest that drugs that improve DNA repeat stability, could thus be explored as a treatment option for SCA12.

Structural and functional implications of ABCC1 variants on clinical statin response

ATP-binding cassette (ABC) proteins are membrane transporters responsible for metabolites and active substances removal from cells. Their genes' variations have been associated with protein function and expression defects. Familial Hypercholesterolemia (FH) patients hosting those alterations might compromise the efficacy of high-dose statin treatment, a primary therapeutic strategy. ABCC1 is a member of the ABC-transporter superfamily, potentially relevant to pharmacological therapy responses and toxicity risks in hypercholesterolemic patients. Here, we evaluated specific non-synonymous (SNV) missense variants in the ABCC1 gene from a FH patient cohort, assessing potential impacts on protein structure, molecular dynamics and interactions with rosuvastatin, atorvastatin, pravastatin, pitavastatin, and lovastatin. Molecular docking, complemented by motion, visual and binding affinity analysis using the PLANNET model, suggested that these mutations had minimal impact on drug interactions. These findings prompted further analysis of two other efflux pumps, ABCG2 and P-gp, and their statin interactions. Interestingly, diminished binding affinities hinted at a compensatory mechanism wherein other transporters might mitigate potential ABCC1 mutation effects, ensuring effective drug efflux. Clinical profiles from the patient cohort did not show a correlation between these variants and clinical outcomes, potentially pointing to the role of alternate drug transporters in statin interaction.

Developmental beta-cell death orchestrates the islet's inflammatory milieu by regulating immune system crosstalk

While pancreatic beta-cell proliferation has been extensively studied, the role of cell death during islet development remains incompletely understood. Using a genetic model of caspase inhibition in beta cells coupled with mathematical modeling, we here discover an onset of beta-cell death in juvenile zebrafish, which regulates beta-cell mass. Histologically, this beta-cell death is underestimated due to phagocytosis by resident macrophages. To investigate beta-cell apoptosis at the molecular level, we implement a conditional model of beta-cell death linked to Ca2+ overload. Transcriptomic analysis reveals that metabolically-stressed beta cells follow paths to either de-differentiation or apoptosis. Beta cells destined to die activate inflammatory and immuno-regulatory pathways, suggesting that cell death regulates the crosstalk with immune cells. Consistently, inhibiting beta-cell death during development reduces pro-inflammatory resident macrophages and expands T-regulatory cells, the deficiency of which causes premature activation of NF-kB signaling in beta cells. Thus, developmental cell death not only shapes beta-cell mass but it also influences the islet's inflammatory milieu by shifting the immune-cell population towards pro-inflammatory.

In silico modelling of organ-on-a-chip devices: an overview

An organ-on-a-chip (OOAC) is a microscale device designed to mimic the functions and complexity of in vivo human physiology. Different from traditional culture systems, OOACs are capable of replicating the biochemical microenvironment, tissue-tissue interactions, and mechanical dynamics of organs thanks to the precise control offered by microfluidic technology. Diverse OOAC devices specific to different organs have been proposed for experimental research and applications such as disease modelling, personalized medicine and drug screening. Previous studies have demonstrated that the mathematical modelling of OOAC can facilitate the optimization of chips' microenvironments, serving as an essential tool to design and improve microdevices which allow reproducible growth of cell culture, reducing the time and cost of experimental testing. Here, we review recent modelling approaches for various OOAC devices, categorized according to the type of organs. We discuss the opportunities for integrating multiphysics with multicellular computational models to better characterize and predict cell culture dynamics. Additionally, we explore how developing more detailed OOAC models would support a more rapid and effective development of microdevices, and the design of robust protocols to grow and control cell cultures.

Personalizing computational models to construct medical digital twins

Digital twin technology, pioneered for engineering applications, is being adapted to biomedicine and healthcare; however, several problems need to be solved in the process. One major problem is that of dynamically calibrating a computational model to an individual patient, using data collected from that patient over time. This kind of calibration is crucial for improving model-based forecasts and realizing personalized medicine. The underlying computational model often focuses on a particular part of human biology, combines different modeling paradigms at different scales, and is both stochastic and spatially heterogeneous. A commonly used modeling framework is that of an agent-based model, a computational model for simulating autonomous agents such as cells, which captures how system-level properties are affected by local interactions. There are no standard personalization methods that can be readily applied to such models. The key challenge for any such algorithm is to bridge the gap between the clinically measurable quantities (the macrostate) and the fine-grained data at different physiological scales which are required to run the model (the microstate). In this paper we develop an algorithm which applies a classic data assimilation technique, the ensemble Kalman filter, at the macrostate level. We then link the Kalman update at the macrostate level to an update at the microstate level that produces microstates which are not only compatible with desired macrostates but also highly likely with respect to model dynamics.

An adaptive numerical method for multi-cellular simulations of tissue development and maintenance

In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development, maintenance, and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate it's efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of first order ordinary differential equations, derived by assuming over-damped motion and balancing forces. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge-Kutta 4 and multi-step methods like Adams-Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between L∞ error and run-time is to use Runge-Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10-60 from the Forward Euler methods seen in Osborne et al. (2017), and a further speed up by a factor of 4 can be achieved by using an adaptive time-step.

Plasma proteomic profiles of patients with HIV infection and coinfection with hepatitis B/C virus undergoing anti‑retroviral therapy

Chronic liver disease is becoming a leading cause of illness and mortality in patients living with human immunodeficiency virus (HIV; PLWH) undergoing suppressive anti-retroviral therapy. Its primary etiology is coinfection with hepatitis B and C virus (HBV and HCV, respectively). Chronic liver inflammation and fibrosis can potentially lead to the development of hepatocellular carcinoma (HCC). Therefore, monitoring of the disease progression in PLWH is required. The present study aimed to explore plasma protein profiles of PLWH and those coinfected with HBV and HCV using shotgun proteomics. HIV-monoinfected, HIV/HBV-coinfected, HIV/HCV-coinfected and uninfected control individuals were recruited. Patients in the three virus-infected groups had significantly higher levels of liver fibrosis indices (fibrosis-4 score and aspartate aminotransferase to platelet ratio index) compared with the control group. Liquid chromatography-tandem mass spectrometry analysis of plasma samples identified 1,074 proteins that were differentially expressed, where subsequent partial least squares-discriminant analysis model demonstrated clear clustering of proteomes from the four sample groups; 18 proteins that were significantly differentially expressed. Heatmap analysis identified two main groups of proteins, six proteins being upregulated only in the HIV/HBV-coinfection group and 10 proteins downregulated in all three virally infected groups. STITCH 5.0 analysis predicted an interaction network containing two identified proteins in the latter group, specifically ubiquitin interaction motif-containing 1 (UIMC1) and haptoglobin (HP), which are part of the profibrogenic TGF-1β/SMAD, inflammatory TNF and tumor suppressor BRCA1 pathways. Expression levels of UIMC1 and HP were significantly lower in HIV-infected groups compared with those in uninfected controls. Altogether, these proteomics data provide protein expression profiles potentially associated with HIV infection and coinfection with HBV/HCV, which may be applied to predict progression to advanced liver disease or HCC in PLWH.

The cellular Potts model on disordered lattices

The cellular Potts model, also known as the Glazier-Graner-Hogeweg model, is a lattice-based approach by which biological tissues at the level of individual cells can be numerically studied. Traditionally, a square or hexagonal underlying lattice structure is assumed for two-dimensional systems, and this is known to introduce artifacts in the structure and dynamics of the model tissues. That is, on regular lattices, cells can assume shapes that are dictated by the symmetries of the underlying lattice. Here, we developed a variant of this method that can be applied to a broad class of (ir)regular lattices. We show that on an irregular lattice deriving from a fluid-like configuration, two types of artifacts can be removed. We further report on the transition between a fluid-like disordered and a solid-like hexagonally ordered phase present for monodisperse confluent cells as a function of their surface tension. This transition shows the hallmarks of a first-order phase transition and is different from the glass/jamming transitions commonly reported for the vertex and active Voronoi models. We emphasize this by analyzing the distribution of shape parameters found in our state space. Our analysis provides a useful reference for the future study of epithelia using the (ir)regular cellular Potts model.

Loss of a single Zn finger, but not that of two Zn fingers, of GATA3 drives skin inflammation

Transcription factor GATA3 is essential for the developmental processes of T cells. Recently, the silencer of a cytokine IFNγ gene was identified, the inhibitory activity of which requires GATA3. GATA3 has 2 Zn fingers and the commonly used GATA3 deficient mice lack both fingers (D2). We have established a mouse line that lacks only one Zn finger close to the C terminus (D1). The D1 mice line developed dermatitis, which was not observed in D2 mice. The expression of S100a8/S100a9 was elevated in D1 to a level higher than in D2, suggesting their roles in dermatitis development. CD8 T cells of both D1 and D2 lines expressed inhibitory receptors associated with the exhausted state. In the absence of MHC class II, the skin inflammation was exacerbated in both lines. The gene expression pattern of CD8 T cells became similar to that of effector T cells. Blocking Ab against LAG3 upregulated the expression of the effector molecules of T cells. These results suggest that the disfunction of GATA3 can lead to the spontaneous activation of CD8 T cells that causes skin inflammation, and that suppressive activity of MHC class II - LAG3 interaction ameliorates dermatitis development.

Building multiscale models with PhysiBoSS, an agent-based modeling tool

Multiscale models provide a unique tool for analyzing complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. These models aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms-coupled with a graphical interface-is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to construct these models more easily, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as Supplementary Material and all models are provided at https://physiboss.github.io/tutorial/.

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport

INTRODUCTION

Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling.

OBJECTIVES

To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature.

METHODS

To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue.

RESULTS

With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined.

CONCLUSION

KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at   http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at   https://deepamahm.github.io/kiphynet_docs/ .

Integrative analysis of bulk and single-cell RNA sequencing reveals the gene expression profile and the critical signaling pathways of type II CPAM

BACKGROUD

Type II congenital pulmonary airway malformation (CPAM) is a rare pulmonary microcystic developmental malformation. Surgical excision is the primary treatment for CPAM, although maternal steroids and betamethasone have proven effective in reducing microcystic CPAM. Disturbed intercellular communication may contribute to the development of CPAM. This study aims to investigate the expression profile and analyze intercellular communication networks to identify genes potentially associated with type II CPAM pathogenesis and therapeutic targets.

METHODS

RNA sequencing (RNA-seq) was performed on samples extracted from both the cystic area and the adjacent normal tissue post-surgery in CPAM patients. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify genes specifically expressed in type II CPAM. Single-cell RNA-seq (scRNA-seq) was integrated to unveil the heterogeneity in cell populations and analyze the communication and interaction within epithelial cell sub-populations.

RESULTS

A total of 2,618 differentially expressed genes were identified, primarily enriched in cilium-related biological process and inflammatory response process. Key genes such as EDN1, GPR17, FPR2, and CHRM1, involved in the G protein-coupled receptor (GPCR) signaling pathway and playing roles in cell differentiation, apoptosis, calcium homeostasis, and the immune response, were highlighted based on the protein-protein interaction network. Type II CPAM-associated modules, including ciliary function-related genes, were identified using iWGCNA. By integrating scRNA-seq data, AGR3 (related to calcium homeostasis) and SLC11A1 (immune related) were identified as the only two differently expressed genes in epithelial cells of CPAM. Cell communication analysis revealed that alveolar type 1 (AT1) and alveolar type 2 (AT2) cells were the predominant communication cells for outgoing and incoming signals in epithelial cells. The ligands and receptors between epithelial cell subtypes included COLLAGEN genes enriched in PI3K-AKT singaling and involved in epithelial to mesenchymal transition.

CONCLUSIONS

In summary, by integrating bulk RNA-seq data of type II CPAM with scRNA-seq data, the gene expression profile and critical signaling pathways such as GPCR signaling and PI3K-AKT signaling pathways were revealed. Abnormally expressed genes in these pathways may disrupt epithelial-mesenchymal transition and contribute to the development of CPAM. Given the effectiveness of prenatal treatments of microcystic CPAM using maternal steroids and maternal betamethasone administration, targeting the genes and signaling pathways involved in the development of CPAM presents a promising therapeutic strategy.

Cell Simulation as Cell Segmentation

Single-cell spatial transcriptomics promises a highly detailed view of a cell's transcriptional state and microenvironment, yet inaccurate cell segmentation can render this data murky by misattributing large numbers of transcripts to nearby cells or conjuring nonexistent cells. We adopt methods from ab initio cell simulation to rapidly infer morphologically plausible cell boundaries that preserve cell type heterogeneity. Benchmarking applied to datasets generated by three commercial platforms show superior performance and computational efficiency of this approach compared with existing methods. We show that improved accuracy in cell segmentation aids greatly in detection of difficult to accurately segment tumor infiltrating immune cells such as neutrophils and T cells. Lastly, through improvements in our ability to delineate subsets of tumor infiltrating T cells, we show that CXCL13-expressing CD8+ T cells tend to be more closely associated with tumor cells than their CXCL13-negative counterparts in data generated from renal cell carcinoma patient samples.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Derivation and simulation of a computational model of active cell populations: How overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces affect alignment

Collective alignment of cell populations is a commonly observed phenomena in biology. An important example are aligning fibroblasts in healthy or scar tissue. In this work we derive and simulate a mechanistic agent-based model of the collective behaviour of actively moving and interacting cells, with a focus on understanding collective alignment. The derivation strategy is based on energy minimisation. The model ingredients are motivated by data on the behaviour of different populations of aligning fibroblasts and include: Self-propulsion, overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces. We find that there is an optimal ratio of self-propulsion speed and overlap avoidance that maximises collective alignment. Further we find that deformability aids alignment, and that cell-cell junctions by themselves hinder alignment. However, if cytoskeletal forces are transmitted via cell-cell junctions we observe strong collective alignment over large spatial scales.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

Toward a predictive understanding of epithelial cell death

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

A diffusible small-RNA-based Turing system dynamically coordinates organ polarity

The formation of a flat and thin leaf presents a developmentally challenging problem, requiring intricate regulation of adaxial-abaxial (top-bottom) polarity. The patterning principles controlling the spatial arrangement of these domains during organ growth have remained unclear. Here we show that this regulation in Arabidopsis thaliana is achieved by an organ-autonomous Turing reaction-diffusion system centred on mobile small RNAs. The data illustrate how Turing dynamics transiently instructed by prepatterned information is sufficient to self-sustain properly oriented polarity in a dynamic, growing organ, presenting intriguing parallels to left-right patterning in the vertebrate embryo. Computational modelling demonstrates that this self-organizing system continuously adapts to coordinate the robust planar polarity of a flat leaf while affording flexibility to generate the tissue patterns of evolutionarily diverse organ shapes. Our findings identify a small-RNA-based Turing network as a dynamic regulator of organ polarity that accounts for leaf shape diversity at the level of the individual organ, plant or species.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Immunological signatures unveiled by integrative systems vaccinology characterization of dengue vaccination trials and natural infection

Introduction

Dengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs).

Methods and results

We analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response.

Conclusion

This work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., IFIT5, ISG15, and HERC5) for anti-dengue-specific therapies and effective vaccination development.

Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.

The shapes of elongating gastruloids are consistent with convergent extension driven by a combination of active cell crawling and differential adhesion

Gastruloids have emerged as highly useful in vitro models of mammalian gastrulation. One of the most striking features of 3D gastruloids is their elongation, which mimics the extension of the embryonic anterior-posterior axis. Although axis extension is crucial for development, the underlying mechanism has not been fully elucidated in mammalian species. Gastruloids provide an opportunity to study this morphogenic process in vitro. Here, we measure and quantify the shapes of elongating gastruloids and show, by Cellular Potts model simulations based on a novel, optimized algorithm, that convergent extension, driven by a combination of active cell crawling and differential adhesion can explain the observed shapes. We reveal that differential adhesion alone is insufficient and also directly observe hallmarks of convergent extension by time-lapse imaging of gastruloids. Finally, we show that gastruloid elongation can be abrogated by inhibition of the Rho kinase pathway, which is involved in convergent extension in vivo. All in all, our study demonstrates, how gastruloids can be used to elucidate morphogenic processes in embryonic development.

Deregulation of ABCG1 early in life contributes to prostate carcinogenesis in maternally malnourished offspring rats

AIMS

The developmental Origins of Health and Disease (DOHaD) concept has provided the framework to assess how early life experiences can shape health and disease throughout the life course. Using a model of maternal exposure to a low protein diet (LPD; 6% protein) during the gestational and lactational periods, we demonstrated changes in the ventral prostate (VP) transcriptomic landscape in young rats exposed to maternal malnutrition. Male offspring Sprague Dawley rats were submitted to maternal malnutrition during gestation and lactation, and they were weighed, and distance anogenital was measured, followed were euthanized by an overdose of anesthesia at 21 postnatal days. Next, the blood and the ventral prostate (VP) were collected and processed by morphological analysis, biochemical and molecular analyses. RNA-seq analysis identified 411 differentially expressed genes (DEGs) in the VP of maternally malnourished offspring compared to the control group. The molecular pathways enriched by these DEGs are related to cellular development, differentiation, and tissue morphogenesis, all of them involved in both normal prostate development and carcinogenesis. Abcg1 was commonly deregulated in young and old maternally malnourished offspring rats, as well in rodent models of prostate cancer (PCa) and in PCa patients. Our results described ABCG1 as a potential DOHaD gene associated with perturbation of prostate developmental biology with long-lasting effects on carcinogenesis in old offspring rats. A better understanding of these mechanisms may help with the discussion of preventive strategies against early life origins of non-communicable chronic diseases.

Stochastic biological system-of-systems modelling for iPSC culture

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.

CellularPotts.jl: simulating multiscale cellular models in Julia

SUMMARY

CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia.

AVAILABILITY AND IMPLEMENTATION

The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.

Generative modeling of living cells with SO(3)-equivariant implicit neural representations

Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. Hopefully the model will be defined using some format specification, e.g., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects, e.g., cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged.

Spatiotemporal expression patterns of anxiety disorder-associated genes

Anxiety disorders (ADs) are the most common form of mental disorder that affects millions of individuals worldwide. Although physiological studies have revealed the neural circuits related to AD symptoms, how AD-associated genes are spatiotemporally expressed in the human brain still remains unclear. In this study, we integrated genome-wide association studies of four human AD subtypes-generalized anxiety disorder, social anxiety disorder, panic disorder, and obsessive-compulsive disorder-with spatial gene expression patterns. Our investigation uncovered a novel division among AD-associated genes, marked by significant and distinct expression enrichments in the cerebral nuclei, limbic, and midbrain regions. Each gene cluster was associated with specific anxiety-related behaviors, signaling pathways, region-specific gene networks, and cell types. Notably, we observed a significant negative correlation in the temporal expression patterns of these gene clusters during various developmental stages. Moreover, the specific brain regions enriched in each gene group aligned with neural circuits previously associated with negative decision-making and anxious temperament. These results suggest that the two distinct gene clusters may underlie separate neural systems involved in anxiety. As a result, our findings bridge the gap between genes and neural circuitry, shedding light on the mechanisms underlying AD-associated behaviors.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Contributions of cell behavior to geometric order in embryonic cartilage

During early development, cartilage provides shape and stability to the embryo while serving as a precursor for the skeleton. Correct formation of embryonic cartilage is hence essential for healthy development. In vertebrate cranial cartilage, it has been observed that a flat and laterally extended macroscopic geometry is linked to regular microscopic structure consisting of tightly packed, short, transversal clonar columns. However, it remains an ongoing challenge to identify how individual cells coordinate to successfully shape the tissue, and more precisely which mechanical interactions and cell behaviors contribute to the generation and maintenance of this columnar cartilage geometry during embryogenesis. Here, we apply a three-dimensional cell-based computational model to investigate mechanical principles contributing to column formation. The model accounts for clonal expansion, anisotropic proliferation and the geometrical arrangement of progenitor cells in space. We confirm that oriented cell divisions and repulsive mechanical interactions between cells are key drivers of column formation. In addition, the model suggests that column formation benefits from the spatial gaps created by the extracellular matrix in the initial configuration, and that column maintenance is facilitated by sequential proliferative phases. Our model thus correctly predicts the dependence of local order on division orientation and tissue thickness. The present study presents the first cell-based simulations of cell mechanics during cranial cartilage formation and we anticipate that it will be useful in future studies on the formation and growth of other cartilage geometries.

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes

MOTIVATION

Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes.

RESULTS

Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology.

AVAILABILITY AND IMPLEMENTATION

FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

Early-life origin of prostate cancer through deregulation of miR-206 networks in maternally malnourished offspring rats

The Developmental Origins of Health and Disease (DOHaD) concept has provided the framework to assess how early life experiences can shape health and disease throughout the life course. While maternal malnutrition has been proposed as a risk factor for the developmental programming of prostate cancer (PCa), the molecular mechanisms remain poorly understood. Using RNA-seq data, we demonstrated deregulation of miR-206-Plasminogen (PLG) network in the ventral prostate (VP) of young maternally malnourished offspring. RT-qPCR confirmed the deregulation of the miR-206-PLG network in the VP of young and old offspring rats. Considering the key role of estrogenic signaling pathways in prostate carcinogenesis, in vitro miRNA mimic studies also revealed a negative correlation between miR-206 and estrogen receptor α (ESR1) expression in PNT2 cells. Together, we demonstrate that early life estrogenization associated with the deregulation of miR-206 networks can contribute to the developmental origins of PCa in maternally malnourished offspring. Understanding the molecular mechanisms by which early life malnutrition affects offspring health can encourage the adoption of a governmental policy for the prevention of non-communicable chronic diseases related to the DOHaD concept.

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst's broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

Tumor-mediated immunosuppression and cytokine spreading affects the relation between EMT and PD-L1 status

Epithelial-mesenchymal transition (EMT) and immune resistance mediated by Programmed Death-Ligand 1 (PD-L1) upregulation are established drivers of tumor progression. Their bi-directional crosstalk has been proposed to facilitate tumor immunoevasion, yet the impact of immunosuppression and spatial heterogeneity on the interplay between these processes remains to be characterized. Here we study the role of these factors using mathematical and spatial models. We first designed models incorporating immunosuppressive effects on T cells mediated via PD-L1 and the EMT-inducing cytokine Transforming Growth Factor beta (TGFβ). Our models predict that PD-L1-mediated immunosuppression merely reduces the difference in PD-L1 levels between EMT states, while TGFβ-mediated suppression also causes PD-L1 expression to correlate negatively with TGFβ within each EMT phenotype. We subsequently embedded the models in multi-scale spatial simulations to explicitly describe heterogeneity in cytokine levels and intratumoral heterogeneity. Our multi-scale models show that Interferon gamma (IFNγ)-induced partial EMT of a tumor cell subpopulation can provide some, albeit limited protection to bystander tumor cells. Moreover, our simulations show that the true relationship between EMT status and PD-L1 expression may be hidden at the population level, highlighting the importance of studying EMT and PD-L1 status at the single-cell level. Our findings deepen the understanding of the interactions between EMT and the immune response, which is crucial for developing novel diagnostics and therapeutics for cancer patients.

Influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics within human airway epithelium

Human airway epithelium (HAE) represents the primary site of viral infection for SARS-CoV-2. Comprising different cell populations, a lot of research has been aimed at deciphering the major cell types and infection dynamics that determine disease progression and severity. However, the cell type-specific replication kinetics, as well as the contribution of cellular composition of the respiratory epithelium to infection and pathology are still not fully understood. Although experimental advances, including Air-liquid interface (ALI) cultures of reconstituted pseudostratified HAE, as well as lung organoid systems, allow the observation of infection dynamics under physiological conditions in unprecedented level of detail, disentangling and quantifying the contribution of individual processes and cells to these dynamics remains challenging. Here, we present how a combination of experimental data and mathematical modelling can be used to infer and address the influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics. Using a stepwise approach that integrates various experimental data on HAE culture systems with regard to tissue differentiation and infection dynamics, we develop an individual cell-based model that enables investigation of infection and regeneration dynamics within pseudostratified HAE. In addition, we present a novel method to quantify tissue integrity based on image data related to the standard measures of transepithelial electrical resistance measurements. Our analysis provides a first aim of quantitatively assessing cell type specific infection kinetics and shows how tissue composition and changes in regeneration capacity, as e.g. in smokers, can influence disease progression and pathology. Furthermore, we identified key measurements that still need to be assessed in order to improve inference of cell type specific infection kinetics and disease progression. Our approach provides a method that, in combination with additional experimental data, can be used to disentangle the complex dynamics of viral infection and immunity within human airway epithelial culture systems.

Deformability and collision-induced reorientation enhance cell topotaxis in dense microenvironments

In vivo, cells navigate through complex environments filled with obstacles such as other cells and the extracellular matrix. Recently, the term "topotaxis" has been introduced for navigation along topographic cues such as obstacle density gradients. Experimental and mathematical efforts have analyzed topotaxis of single cells in pillared grids with pillar density gradients. A previous model based on active Brownian particles (ABPs) has shown that ABPs perform topotaxis, i.e., drift toward lower pillar densities, due to decreased effective persistence lengths at high pillar densities. The ABP model predicted topotactic drifts of up to 1% of the instantaneous speed, whereas drifts of up to 5% have been observed experimentally. We hypothesized that the discrepancy between the ABP and the experimental observations could be in 1) cell deformability and 2) more complex cell-pillar interactions. Here, we introduce a more detailed model of topotaxis based on the cellular Potts model (CPM). To model persistent cells we use the Act model, which mimics actin-polymerization-driven motility, and a hybrid CPM-ABP model. Model parameters were fitted to simulate the experimentally found motion of Dictyostelium discoideum on a flat surface. For starved D. discoideum, the topotactic drifts predicted by both CPM variants are closer to the experimental results than the previous ABP model due to a larger decrease in persistence length. Furthermore, the Act model outperformed the hybrid model in terms of topotactic efficiency, as it shows a larger reduction in effective persistence time in dense pillar grids. Also pillar adhesion can slow down cells and decrease topotaxis. For slow and less-persistent vegetative D. discoideum cells, both CPMs predicted a similar small topotactic drift. We conclude that deformable cell volume results in higher topotactic drift compared with ABPs, and that feedback of cell-pillar collisions on cell persistence increases drift only in highly persistent cells.

Microbiome analysis reveals potential for modulation of gut microbiota through polysaccharide-based prebiotic feeding in Oreochromis niloticus (Linnaeus, 1758)

Characterization and functional profiling of the gut microbiota are essential for guiding nutritional interventions in fish and achieving favorable host-microbe interactions. Thus, we conducted a 30 days study to explore and document the gut microbial community of O. niloticus, as well as to evaluate the effects of a polysaccharide-based prebiotics with 0.5% and 0.75% Aloe vera extract on the gut microbiome through genomic analysis. The V3-V4 region of 16S rRNA was amplified and sequenced using Illumina HiSeq 2500, resulting in 1,000,199 reads for operational taxonomic unit (OTU) identification. Out of 8,894 OTUs, 1,181 were selected for further analysis. Our results revealed that Planctomycetes, Firmicutes, Proteobacteria, Verrucomicrobia, Actinobacteria, and Fusobacteria were the dominant phyla in both control and treatment samples. Higher doses of prebiotics were found to improve Planctomycetes and Firmicutes while decreasing Proteobacteria and Verrucomicrobia. We observed increasing trends in the abundance of Bacilli, Bacillaceae, and Bacillus bacteria at the class, family, and genus levels, respectively, in a dose-dependent manner. These findings were consistent with the conventional colony count data, which showed a higher prevalence of Bacillus in prebiotic-supplemented groups. Moreover, predicted functional analysis using PICRUSt indicated a dose-dependent upregulation in glycolysis V, superpathway of glycol metabolism and degradation, glucose and xylose degradation, glycolysis II, and sulfoglycolysis pathways. Most of the energy, protein, and amino acid synthesis pathways were upregulated only at lower doses of prebiotic treatment. Our findings suggest that the gut microbiome of O. niloticus can be optimized through nutritional interventions with plant-based polysaccharides for improved growth performance in commercial fish.

Pinostrobin induces acute leukemia cell apoptosis via the regulation of miR-410-5p and SFRP5

AIMS

This study attempted to explore the mechanisms involved in pinostrobin (PN)-mediated acute leukemia cell apoptosis regulated by miR-410-5p.

MATERIAL AND METHODS

NB4 and MOLT-4 cells were cultured and treated with PN at the IC50 concentration. Apoptosis was examined by Annexin V-FITC/PI staining. RT-qPCR was used to measure the expression of caspase-3, BAK, BCL-W, and MCL-1. The target protein of PN was identified using LC-MS/MS followed by bioinformatic analysis. TargetScan, DIANA, and miRDB were used for the prediction of miRNAs involved in the PN-induced apoptosis mechanism. miRNA mimic transfection, RT-qPCR, and western blot analysis were performed to evaluate the regulatory effect of miRNA on its target and the involvement of miRNA in apoptosis induction by PN. In addition, the synergistic effect of PN and daunorubicin (DNR) were investigated by using the MTT assay.

KEY FINDINGS

The results showed that PN reduced cell viability and induced apoptosis in both leukemia cell lines. From the LC-MS/MS and bioinformatics analysis, SFRP5 and miR-410-5p were selected as a potential PN target protein and miRNA, respectively. After miRNA mimic transfection, miR-410-5p, which is an onco-miRNA, was decreased and led to increased apoptosis in both cell lines, indicating that this miRNA is involved in PN-mediated apoptosis mechanisms. Moreover, PN demonstrated a synergistic effect with DNR, suggesting that PN may be used in combination with conventional chemotherapy drugs.

SIGNIFICANCE

PN regulates the expression of miR-410-5p and SFRP5 to promote apoptosis in acute leukemia cells. It could be developed as an alternative treatment for leukemia in the future.

Excitable dynamics in a molecularly-explicit model of cell motility: Mixed-mode oscillations and beyond

Mesenchymal cell motility is mainly regulated by two members of the Rho-family of GTPases, called Rac and Rho. The mutual inhibition exerted by these two proteins on each other's activation and the promotion of Rac activation by an adaptor protein called paxillin have been implicated in driving cellular polarization comprised of front (high active Rac) and back (high active Rho) during cell migration. Mathematical modeling of this regulatory network has previously shown that bistability is responsible for generating a spatiotemporal pattern underscoring cellular polarity called wave-pinning when diffusion is included. We previously developed a 6V reaction-diffusion model of this network to decipher the role of Rac, Rho and paxillin (along with other auxiliary proteins) in generating wave-pinning. In this study, we simplify this model through a series of steps into an excitable 3V ODE model comprised of one fast variable (the scaled concentration of active Rac), one slow variable (the maximum paxillin phosphorylation rate - turned into a variable) and a very slow variable (a recovery rate - also turned into a variable). We then explore, through slow-fast analysis, how excitability is manifested by showing that the model can exhibit relaxation oscillations (ROs) as well as mixed-mode oscillations (MMOs) whose underlying dynamics are consistent with a delayed Hopf bifurcation with a canard explosion. By reintroducing diffusion and the scaled concentration of inactive Rac into the model, we obtain a 4V PDE model that generates several unique spatiotemporal patterns that are relevant to cell motility. These patterns are then characterized and their impact on cell motility are explored by employing the cellular potts model (CPM). Our results reveal that wave pinning produces purely very directed motion in CPM, while MMOs allow for meandering and non-motile behaviors to occur. This highlights the role of MMOs as a potential mechanism for mesenchymal cell motility.

Polarity and mixed-mode oscillations may underlie different patterns of cellular migration

In mesenchymal cell motility, several migration patterns have been observed, including directional, exploratory and stationary. Two key members of the Rho-family of GTPases, Rac and Rho, along with an adaptor protein called paxillin, have been particularly implicated in the formation of such migration patterns and in regulating adhesion dynamics. Together, they form a key regulatory network that involves the mutual inhibition exerted by Rac and Rho on each other and the promotion of Rac activation by phosphorylated paxillin. Although this interaction is sufficient in generating wave-pinning that underscores cellular polarization comprised of cellular front (high active Rac) and back (high active Rho), it remains unclear how they interact collectively to induce other modes of migration detected in Chinese hamster Ovary (CHO-K1) cells. We previously developed a six-variable (6V) reaction-diffusion model describing the interactions of these three proteins (in their active/phosphorylated and inactive/unphosphorylated forms) along with other auxiliary proteins, to decipher their role in generating wave-pinning. In this study, we explored, through computational modeling and image analysis, how differences in timescales within this molecular network can potentially produce the migration patterns in CHO-K1 cells and how switching between migration modes could occur. To do so, the 6V model was reduced to an excitable 4V spatiotemporal model possessing three different timescales. The model produced not only wave-pinning in the presence of diffusion, but also mixed-mode oscillations (MMOs) and relaxation oscillations (ROs). Implementing the model using the Cellular Potts Model (CPM) produced outcomes in which protrusions in the cell membrane changed Rac-Rho localization, resulting in membrane oscillations and fast directionality variations similar to those observed experimentally in CHO-K1 cells. The latter was assessed by comparing the migration patterns of experimental with CPM cells using four metrics: instantaneous cell speed, exponent of mean-square displacement ([Formula: see text]-value), directionality ratio and protrusion rate. Variations in migration patterns induced by mutating paxillin's serine 273 residue were also captured by the model and detected by a machine classifier, revealing that this mutation alters the dynamics of the system from MMOs to ROs or nonoscillatory behaviour through variation in the scaled concentration of an active form of an adhesion protein called p21-Activated Kinase 1 (PAK). These results thus suggest that MMOs and adhesion dynamics are the key mechanisms regulating CHO-K1 cell motility.

Tissue interplay during morphogenesis

The process by which biological systems such as cells, tissues and organisms acquire shape has been named as morphogenesis and it is central to a plethora of biological contexts including embryo development, wound healing, or even cancer. Morphogenesis relies in both self-organising properties of the system and in environmental inputs (biochemical and biophysical). The classical view of morphogenesis is based on the study of external biochemical molecules, such as morphogens. However, recent studies are establishing that the mechanical environment is also used by cells to communicate within tissues, suggesting that this mechanical crosstalk is essential to synchronise morphogenetic transitions and self-organisation. In this article we discuss how tissue interaction drive robust morphogenesis, starting from a classical biochemical view, to finalise with more recent advances on how the biophysical properties of a tissue feedback with their surroundings to allow form acquisition. We also comment on how in silico models aid to integrate and predict changes in cell and tissue behaviour. Finally, considering recent advances from the developmental biomechanics field showing that mechanical inputs work as cues that promote morphogenesis, we invite to revisit the concept of morphogen.

Age-dependent impairment in antibody responses elicited by a homologous CoronaVac booster dose

The emergence of the SARS-CoV-2 Omicron sublineages resulted in increased transmission rates and reduced protection from vaccines. To counteract these effects, multiple booster strategies were used in different countries, although data comparing their efficiency in improving protective immunity remain sparse, especially among vulnerable populations, including older adults. The inactivated CoronaVac vaccine was among the most widely distributed vaccine worldwide and was essential in the early control of SARS-CoV-2-related hospitalizations and deaths. However, it is not well understood whether homologous versus heterologous booster doses in those fully vaccinated with CoronaVac induce distinct humoral responses or whether these responses vary across age groups. We analyzed plasma antibody responses from CoronaVac-vaccinated younger or older individuals who received a homologous CoronaVac or heterologous BNT162b2 or ChAdOx1 booster vaccine. All three evaluated boosters resulted in increased virus-specific IgG titers 28 days after the booster dose. However, we found that both IgG titers against SARS-CoV-2 Spike or RBD and neutralization titers against Omicron sublineages were substantially reduced in participants who received homologous CoronaVac compared with the heterologous BNT162b2 or ChAdOx1 booster. This effect was specifically prominent in recipients >50 years of age. In this group, the CoronaVac booster induced low virus-specific IgG titers and failed to elevate neutralization titers against any Omicron sublineage. Our results point to the notable inefficiency of CoronaVac immunization and boosting in mounting protective antiviral humoral immunity, particularly among older adults, during the Omicron wave. These observations also point to benefits of heterologous regimens in high-risk populations fully vaccinated with CoronaVac.

Hybrid computational models of multicellular tumour growth considering glucose metabolism

Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.